113941 -

: The paper introduces Confident Itemsets Explanation (CIE) , a model-agnostic method that identifies sets of features (words or tokens) that strongly influence a model's prediction.

The identifier refers to a specific research article titled "Post-hoc explanation of black-box classifiers using confident itemsets" , published in the journal Expert Systems with Applications (Volume 165, March 2021). Key Details of the Research Authors : Milad Moradi and Matthias Samwald. 113941

: It addresses the "black-box" problem where complex neural networks provide accurate results but lack transparency, which is critical for high-stakes fields like healthcare. Understanding "Deep Text" : The paper introduces Confident Itemsets Explanation (CIE)